Vetted Machine Learning Professionals

Ayush Gupta

Screened

Mid-level AI Engineer specializing in Agentic AI and Generative AI

6y exp

“Built and deployed a live LLM-powered platform that takes a LinkedIn job URL + resume and generates job-specific resumes and personalized outreach at scale, with production-grade logging/monitoring/retries on Vercel + Railway. Experienced with agent orchestration (AWS Bedrock/Strands, LangGraph, CrewAI) and rigorous AI workflow testing, plus stakeholder-facing prototypes like data lineage/metadata and NL-to-SQL + dashboard generation.”

Priyanshu Maurya

Screened

Mid-level Data Scientist specializing in insurance, finance, and healthcare analytics

New York, NY3y exp

“Built and productionized LLM-driven sentiment scoring for earnings call transcripts at Goldman Sachs, replacing legacy NLP to deliver a cleaner trading signal while managing latency/cost via batching, caching, and distilled models. Also implemented an Airflow-orchestrated fraud modeling pipeline at MetLife with drift-based retraining and SageMaker deployment, and has a disciplined evaluation/rollout framework for reliable AI workflows.”

“Full-stack engineer with fintech and AI product experience: built HuddleAI end-to-end on Firebase/React, including a serverless LLM meeting-intelligence pipeline (FFmpeg + Google Speech-to-Text + GPT-4 with schema validation) and Slack notifications. At Gemini, owned a Postgres/Scala workflow change for wire deposit approvals that cut blocked registrations by 60% and emphasized correctness/compliance in UK/EU transaction-state UI.”

Luis Mirantes

Screened

Executive engineering leader specializing in scaling ML platforms and FinTech systems

Chevy Chase, MD30y exp

“Founder of an AI-agent startup focused on high-speed train braking efficiency, with a subsystem already demoed to RENFE Spain and positive initial feedback. Has hands-on fundraising and investor presentation experience, and stands out for a highly analytical approach to venture building centered on ROI, competitive barriers, IP protection, and evidence-based decision making.”

“ML engineer with production experience across healthcare and fraud domains, including end-to-end ownership of a telecare patient deterioration system at Oracle Health and a GPT-4/RAG fraud reporting solution at Cognizant. Stands out for combining scalable data/ML infrastructure, clinical NLP, and GenAI delivery with measurable gains in model quality and workflow efficiency.”

“Data Analyst at U.S. Cellular who built production LLM solutions, including a Tableau-embedded chatbot that converts natural language questions into Oracle SQL and returns actionable KPI insights for non-technical users. Also authored MAD-CTI, a multi-agent LLM system for dark web hacker forum threat intelligence (published in IEEE Access) that outperformed single-agent approaches by 14%.”

Jyotshna Dammu

Screened

Intern Autonomous Driving Software Engineer specializing in ADAS controls and embedded systems

Auburn Hills, MI3y exp

“Embedded control and systems-integration engineer with hands-on experience taking control logic from Simulink simulation through embedded implementation and rigorous MIL/SIL/HIL plus vehicle testing. Has built and tuned nonlinear/decentralized MPC for autonomous driving behaviors (obstacle avoidance, overtaking, multi-agent coordination) and has CI/CD experience (Jenkins/Git); currently ramping into ROS2 with Python/C++.”

Adam Jones

Screened

Executive Technology Leader specializing in omnichannel digital platforms and product strategy

Chicago, IL16y exp

“Founder of Token Events and former Executive Director of Technology in digital advertising who leveraged large datasets to identify a market gap in first-party/affinity data and digital rewards. Has multiple patents, raised $2.2M, built a beta product, and secured a signed LOI/MSA while launching into the Austin, TX market.”

Pratik Jaiswal

Screened

Mid-level AI/ML Engineer specializing in financial services ML and MLOps

Remote, USA4y exp

“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”

“Backend/data engineer with hands-on production experience across FastAPI/PostgreSQL APIs and AWS (Lambda, ECS) delivered via Terraform + GitHub Actions. Built Glue-based ETL pipelines into Redshift with schema evolution and data quality checks, modernized legacy reporting into Python microservices, and has demonstrated measurable SQL performance wins (multi-second query reduced to sub-300ms).”